docker.io/quantconnect/lean:latest linux/amd64

Docker拉取命令 无权限下载?点我修复

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest  docker.io/quantconnect/lean:latest

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest  docker.io/quantconnect/lean:latest

Shell快速替换命令

sed -i 's#quantconnect/lean:latest#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest#' deployment.yaml

Ansible快速分发-Docker

#ansible k8s -m shell -a 'docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest  docker.io/quantconnect/lean:latest'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/quantconnect/lean:latest  docker.io/quantconnect/lean:latest'

镜像构建历史

# 2025-02-15 05:51:04  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["dotnet" "QuantConnect.Lean.Launcher.dll"]
                        
# 2025-02-15 05:51:04  0.00B 设置工作目录为/Lean/Launcher/bin/Debug
WORKDIR /Lean/Launcher/bin/Debug
                        
# 2025-02-15 05:51:04  37.70MB 复制新文件或目录到容器中
COPY ./Lean/DownloaderDataProvider/bin/Debug/ /Lean/DownloaderDataProvider/bin/Debug/ # buildkit
                        
# 2025-02-15 05:51:04  48.44MB 复制新文件或目录到容器中
COPY ./Lean/Report/bin/Debug/ /Lean/Report/bin/Debug/ # buildkit
                        
# 2025-02-15 05:51:03  24.97MB 复制新文件或目录到容器中
COPY ./Lean/Optimizer.Launcher/bin/Debug/ /Lean/Optimizer.Launcher/bin/Debug/ # buildkit
                        
# 2025-02-15 05:51:03  82.37MB 复制新文件或目录到容器中
COPY ./Lean/Launcher/bin/Debug/ /Lean/Launcher/bin/Debug/ # buildkit
                        
# 2025-02-11 04:27:30  235.65MB 复制新文件或目录到容器中
COPY ./Lean/Data/ /Lean/Data/ # buildkit
                        
# 2025-02-11 04:27:30  52.46MB 复制新文件或目录到容器中
COPY ./DataLibraries /Lean/Launcher/bin/Debug/ # buildkit
                        
# 2025-02-11 04:27:30  200.10MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget https://aka.ms/getvsdbgsh -O - 2>/dev/null | /bin/sh /dev/stdin -v 17.10.20209.7 -l /root/vsdbg # buildkit
                        
# 2025-02-11 04:27:25  100.80MB 执行命令并创建新的镜像层
RUN /bin/sh -c pip install --no-cache-dir ptvsd==4.3.2 debugpy~=1.6.7 pydevd-pycharm~=231.9225.15 # buildkit
                        
# 2025-02-11 04:27:25  0.00B 
MAINTAINER QuantConnect <contact@quantconnect.com>
                        
# 2025-02-11 03:30:17  0.00B 添加元数据标签
LABEL target_framework=net9.0
                        
# 2025-02-11 03:30:17  0.00B 添加元数据标签
LABEL python_version=3.11
                        
# 2025-02-11 03:30:17  0.00B 添加元数据标签
LABEL strict_python_version=3.11.11
                        
# 2025-02-11 03:30:17  451.85MB 执行命令并创建新的镜像层
RUN /bin/sh -c mkdir -p /root/ibgateway &&     wget -q https://cdn.quantconnect.com/interactive/ibgateway-stable-standalone-linux-x64.v10.19.2a.sh &&     chmod 777 ibgateway-stable-standalone-linux-x64.v10.19.2a.sh &&     ./ibgateway-stable-standalone-linux-x64.v10.19.2a.sh -q -dir /root/ibgateway &&     rm ibgateway-stable-standalone-linux-x64.v10.19.2a.sh # buildkit
                        
# 2025-02-11 03:29:53  90.13MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/fonts/foundation.zip && unzip -q foundation.zip && rm foundation.zip     && mv "lean fonts/"* /usr/share/fonts/truetype/ && rm -rf "lean fonts/" "__MACOSX/" # buildkit
                        
# 2025-02-11 03:29:51  203.45MB 执行命令并创建新的镜像层
RUN /bin/sh -c apt-get update && apt install -y xvfb wkhtmltopdf &&     apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2025-02-11 03:29:41  159.00B 执行命令并创建新的镜像层
RUN /bin/sh -c echo "{\"argv\":[\"python\",\"-m\",\"ipykernel_launcher\",\"-f\",\"{connection_file}\"],\"display_name\":\"Foundation-Py-Default\",\"language\":\"python\",\"metadata\":{\"debugger\":true}}" > /opt/miniconda3/share/jupyter/kernels/python3/kernel.json # buildkit
                        
# 2025-02-11 03:29:41  90.14KB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/chronos-forecasting/chronos-forecasting-main-133761a.zip &&     unzip -q chronos-forecasting-main-133761a.zip && cd chronos-forecasting-main &&     cp -r scripts /opt/miniconda3/lib/python3.11/site-packages/chronos/ &&     cd .. && rm -rf chronos-forecasting-main && rm chronos-forecasting-main-133761a.zip # buildkit
                        
# 2025-02-11 03:29:41  13.93MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/ta-lib/ta-lib-0.4.0-src.tar.gz &&     tar -zxvf ta-lib-0.4.0-src.tar.gz && cd ta-lib &&     ./configure --prefix=/usr && make && make install &&     cd .. && rm -rf ta-lib && rm ta-lib-0.4.0-src.tar.gz &&     pip install --no-cache-dir TA-Lib==0.5.1 # buildkit
                        
# 2025-02-11 03:28:34  1.60MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/ssm/ssm-master-646e188.zip &&     unzip -q ssm-master-646e188.zip && cd ssm-master &&     pip install . && cd .. && rm -rf ssm-master && rm ssm-master-646e188.zip # buildkit
                        
# 2025-02-11 03:28:30  1.97MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/pyrb/pyrb-master-250054e.zip &&     unzip -q pyrb-master-250054e.zip && cd pyrb-master &&     pip install . && cd .. && rm -rf pyrb-master && rm pyrb-master-250054e.zip # buildkit
                        
# 2025-02-11 03:28:28  77.15MB 执行命令并创建新的镜像层
RUN /bin/sh -c python -m nltk.downloader -d /usr/share/nltk_data punkt &&     python -m nltk.downloader -d /usr/share/nltk_data punkt_tab &&     python -m nltk.downloader -d /usr/share/nltk_data vader_lexicon &&     python -m nltk.downloader -d /usr/share/nltk_data stopwords &&     python -m nltk.downloader -d /usr/share/nltk_data wordnet # buildkit
                        
# 2025-02-11 03:28:26  16.47MB 执行命令并创建新的镜像层
RUN /bin/sh -c TORCH=$(python -c "import torch; print(torch.__version__)") &&     CUDA=$(python -c "import torch; print('cu' + torch.version.cuda.replace('.', ''))") &&     pip install --no-cache-dir -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html     torch-scatter==2.1.2 torch-sparse==0.6.18 torch-cluster==1.6.3 torch-spline-conv==1.2.2 torch-geometric==2.6.1 # buildkit
                        
# 2025-02-11 03:21:40  126.01MB 执行命令并创建新的镜像层
RUN /bin/sh -c python -m spacy download en_core_web_md && python -m spacy download en_core_web_sm # buildkit
                        
# 2025-02-11 03:21:35  561.05MB 执行命令并创建新的镜像层
RUN /bin/sh -c conda install -c conda-forge -y ipopt==3.14.16 coincbc==2.10.12 openmpi=5.0.6        && conda clean -y --all # buildkit
                        
# 2025-02-11 03:20:48  3.71MB 执行命令并创建新的镜像层
RUN /bin/sh -c dwave install --all -y # buildkit
                        
# 2025-02-11 03:20:46  885.72MB 执行命令并创建新的镜像层
RUN /bin/sh -c pip install --no-cache-dir nvidia-cudnn-cu12==9.3.0.75 # buildkit
                        
# 2025-02-11 03:20:37  3.89GB 执行命令并创建新的镜像层
RUN /bin/sh -c pip install --no-cache-dir iisignature==0.24 cupy-cuda12x==13.3.0 mamba-ssm[causal-conv1d]==2.2.4 # buildkit
                        
# 2025-02-11 03:19:39  84.00B 执行命令并创建新的镜像层
RUN /bin/sh -c ln -s /opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12 /opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_runtime/lib/libcudart.so # buildkit
                        
# 2025-02-11 03:19:39  0.00B 设置环境变量 PYTORCH_CUDA_ALLOC_CONF
ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
                        
# 2025-02-11 03:19:39  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cublas/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_cupti/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_nvrtc/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cuda_runtime/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cudnn/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cufft/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/curand/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cusolver/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/cusparse/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/nccl/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/nvjitlink/lib/:/opt/miniconda3/lib/python3.11/site-packages/nvidia/nvtx/lib/
                        
# 2025-02-11 03:19:39  0.00B 设置环境变量 XLA_FLAGS
ENV XLA_FLAGS=--xla_gpu_cuda_data_dir=/opt/miniconda3/
                        
# 2025-02-11 03:19:39  0.00B 设置环境变量 CUDA_MODULE_LOADING
ENV CUDA_MODULE_LOADING=LAZY
                        
# 2025-02-11 03:19:39  226.72MB 执行命令并创建新的镜像层
RUN /bin/sh -c conda install -c nvidia -y cuda-compiler=12.3.2 && conda clean -y --all # buildkit
                        
# 2025-02-11 03:19:25  17.25GB 执行命令并创建新的镜像层
RUN /bin/sh -c pip install --no-cache-dir          cython==3.0.9                       pandas==2.1.4                       scipy==1.11.4                       numpy==1.26.4                       wrapt==1.16.0                       astropy==7.0.0                      beautifulsoup4==4.12.3              dill==0.3.8                         jsonschema==4.23.0                  lxml==5.3.0                         msgpack==1.1.0                      numba==0.59.1                       xarray==2024.11.0                   plotly==5.24.1                      jupyterlab==4.3.2                   ipywidgets==8.1.5                   jupyterlab-widgets==3.0.13          tensorflow==2.18.0                  docutils==0.21.2                    cvxopt==1.3.2                       gensim==4.3.3                       keras==3.7.0                        lightgbm==4.5.0                     nltk==3.9.1                         graphviz==0.20.3                    cmdstanpy==1.2.4                    copulae==0.7.9                      featuretools==1.31.0                PuLP==2.9.0                         pymc==5.19.0                        rauth==0.7.3                        scikit-learn==1.4.2                 scikit-optimize==0.10.2             aesara==2.9.4                       tsfresh==0.20.2                     tslearn==0.6.3                      tweepy==4.14.0                      PyWavelets==1.7.0                   umap-learn==0.5.7                   fastai==2.7.18                      arch==7.2.0                         copulas==0.12.0                     creme==0.6.1                        cufflinks==0.17.3                   gym==0.26.2                         deap==1.4.1                         pykalman==0.9.7                     cvxpy==1.6.0                        pyportfolioopt==1.5.6               pmdarima==2.0.4                     pyro-ppl==1.9.1                     riskparityportfolio==0.6.0          sklearn-json==0.1.0                 statsmodels==0.14.4                 QuantLib==1.36                      xgboost==2.1.3                      dtw-python==1.5.3                   gluonts==0.16.0                     gplearn==0.4.2                      jax==0.4.35                         jaxlib==0.4.35                      keras-rl==0.4.2                     pennylane==0.39.0                   PennyLane-Lightning==0.39.0         pennylane-qiskit==0.36.0            qiskit==1.2.4                       neural-tangents==0.6.5              mplfinance==0.12.10b0               hmmlearn==0.3.3                     catboost==1.2.7                     fastai2==0.0.30                     scikit-tda==1.1.1                   ta==0.11.0                          seaborn==0.13.2                     optuna==4.1.0                       findiff==0.10.2                     sktime==0.26.0                      hyperopt==0.2.7                     bayesian-optimization==2.0.0        pingouin==0.5.5                     quantecon==0.7.2                    matplotlib==3.7.5                   sdeint==0.3.0                       pandas_market_calendars==4.4.2      dgl==2.1.0                          ruptures==1.1.9                     simpy==4.1.1                        scikit-learn-extra==0.3.0           ray==2.40.0                         "ray[tune]"==2.40.0                 "ray[rllib]"==2.40.0                "ray[data]"==2.40.0                 "ray[train]"==2.40.0                fastText==0.9.3                     h2o==3.46.0.6                       prophet==1.1.6                      torch==2.5.1                        torchvision==0.20.1                 ax-platform==0.4.3                  alphalens-reloaded==0.4.5           pyfolio-reloaded==0.9.8             altair==5.5.0                       modin==0.26.1                       persim==0.3.7                       ripser==0.6.10                      pydmd==2024.12.1                    spacy==3.7.5                        pandas-ta==0.3.14b                  pytorch-ignite==0.5.1               tensorly==0.9.0                     mlxtend==0.23.3                     shap==0.46.0                        lime==0.2.0.1                       tensorflow-probability==0.25.0      mpmath==1.3.0                       tensortrade==1.0.3                  polars==1.16.0                      stockstats==0.6.2                   autokeras==2.0.0                    QuantStats==0.0.64                  hurst==0.0.5                        numerapi==2.19.1                    pymdptoolbox==4.0-b3                panel==1.5.4                        hvplot==0.11.1                      line-profiler==4.2.0                py-heat==0.0.6                      py-heat-magic==0.0.2                bokeh==3.6.2                        tensorflow-decision-forests==1.11.0     river==0.21.0                       stumpy==1.13.0                      pyvinecopulib==0.6.5                ijson==3.3.0                        jupyter-resource-usage==1.1.0       injector==0.22.0                    openpyxl==3.1.5                     xlrd==2.0.1                         mljar-supervised==1.1.9             dm-tree==0.1.8                      lz4==4.3.3                          ortools==9.9.3963                   py_vollib==1.0.1                    thundergbm==0.3.17                  yellowbrick==1.5                    livelossplot==0.5.5                 gymnasium==1.0.0                    interpret==0.6.7                    DoubleML==0.9.0                     jupyter-bokeh==4.0.5                imbalanced-learn==0.12.4            openai==1.57.0                      lazypredict==0.2.14a1               darts==0.31.0                       fastparquet==2024.11.0              tables==3.10.1                      dimod==0.12.17                      dwave-samplers==1.3.0               python-statemachine==2.5.0          pymannkendall==1.4.3                Pyomo==6.8.2                        gpflow==2.9.2                       pyarrow==15.0.1                     dwave-ocean-sdk==8.0.1              chardet==5.2.0                      stable-baselines3==2.4.0            Shimmy==2.0.0                       pystan==3.10.0                      FixedEffectModel==0.0.5             transformers==4.46.3                Rbeast==0.1.23                      langchain==0.2.17                   pomegranate==1.1.1                  MAPIE==0.9.1                        mlforecast==0.15.1                  tensorrt==10.7.0                    x-transformers==1.42.24             Werkzeug==3.1.3                     TPOT==0.12.2                        llama-index==0.12.2                 mlflow==2.18.0                      ngboost==0.5.1                      pycaret==3.3.2                      control==0.10.1                     pgmpy==0.1.26                       mgarch==0.3.0                       jupyter-ai==2.28.2                  keras-tcn==3.5.0                    neuralprophet[live]==0.9.0          Riskfolio-Lib==6.1.1                fuzzy-c-means==1.7.2                EMD-signal==1.6.4                   dask[complete]==2024.9.0            nolds==0.6.1                        feature-engine==1.6.2               pytorch-tabnet==4.1.0               opencv-contrib-python-headless==4.10.0.84     POT==0.9.5                          alibi-detect==0.12.0                datasets==2.21.0                    scikeras==0.13.0                    accelerate==0.34.2                  peft==0.13.2                        FlagEmbedding==1.2.11               contourpy==1.3.1                    tensorboardX==2.6.2.2               scikit-image==0.22.0                scs==3.2.7                          thinc==8.2.5                        cesium==0.12.1                      cvxportfolio==1.4.0                 tsfel==0.1.9                        ipympl==0.9.4                       PyQt6==6.7.1                        nixtla==0.6.4                       tigramite==5.2.6.7                  pytorch-forecasting==1.2.0          chronos-forecasting[training]==1.4.1     setuptools==73.0.1                  tinygrad==0.10.0 # buildkit
                        
# 2025-01-14 05:02:42  0.00B 设置环境变量 PIP_DEFAULT_TIMEOUT
ENV PIP_DEFAULT_TIMEOUT=120
                        
# 2025-01-14 05:02:42  320.14MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget https://download.oracle.com/java/17/archive/jdk-17.0.12_linux-x64_bin.deb     && dpkg -i jdk-17.0.12_linux-x64_bin.deb     && update-alternatives --install /usr/bin/java java /usr/lib/jvm/jdk-17.0.12-oracle-x64/bin/java 1     && rm jdk-17.0.12_linux-x64_bin.deb # buildkit
                        
# 2025-01-14 05:02:31  620.80MB 执行命令并创建新的镜像层
RUN /bin/sh -c wget -q https://cdn.quantconnect.com/miniconda/${CONDA} &&     bash ${CONDA} -b -p /opt/miniconda3 && rm -rf ${CONDA} &&     conda config --set solver classic &&     conda config --set auto_update_conda false # buildkit
                        
# 2025-01-14 05:02:22  0.00B 设置环境变量 PATH
ENV PATH=/opt/miniconda3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2025-01-14 05:02:22  0.00B 设置环境变量 CONDA
ENV CONDA=Miniconda3-py311_24.9.2-0-Linux-x86_64.sh
                        
# 2025-01-14 05:02:22  0.00B 设置环境变量 PYTHONNET_PYDLL
ENV PYTHONNET_PYDLL=/opt/miniconda3/lib/libpython3.11.so
                        
# 2025-01-14 05:02:22  489.55MB 执行命令并创建新的镜像层
RUN /bin/sh -c add-apt-repository ppa:dotnet/backports && apt-get update && apt-get install -y dotnet-sdk-9.0 &&     apt-get clean && apt-get autoclean && apt-get autoremove --purge -y && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2025-01-14 05:00:03  860.52MB 执行命令并创建新的镜像层
RUN /bin/sh -c apt-get update && apt-get -y install wget curl unzip    && apt-get install -y git bzip2 zlib1g-dev    xvfb libxrender1 libxtst6 libxi6 libglib2.0-dev libopenmpi-dev libstdc++6 openmpi-bin    pandoc libcurl4-openssl-dev libgtk2.0.0 build-essential    && apt-get clean && apt-get autoclean && apt-get autoremove --purge -y    && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2025-01-14 05:00:03  0.00B 设置默认要执行的命令
CMD ["/sbin/my_init"]
                        
# 2025-01-14 05:00:03  0.00B 
MAINTAINER QuantConnect <contact@quantconnect.com>
                        
# 2022-09-17 12:41:51  0.00B 设置默认要执行的命令
CMD ["/sbin/my_init"]
                        
# 2022-09-17 12:41:51  0.00B 设置环境变量 DEBIAN_FRONTEND LANG LANGUAGE LC_ALL
ENV DEBIAN_FRONTEND=teletype LANG=en_US.UTF-8 LANGUAGE=en_US:en LC_ALL=en_US.UTF-8
                        
# 2022-09-17 12:41:51  150.71MB 执行命令并创建新的镜像层
RUN |1 QEMU_ARCH= /bin/sh -c /bd_build/prepare.sh && 	/bd_build/system_services.sh && 	/bd_build/utilities.sh && 	/bd_build/cleanup.sh # buildkit
                        
# 2022-09-17 12:39:21  40.41KB 复制新文件或目录到容器中
COPY . /bd_build # buildkit
                        
# 2022-09-17 12:39:21  0.00B 定义构建参数
ARG QEMU_ARCH
                        
# 2022-09-02 07:46:35  0.00B 
/bin/sh -c #(nop)  CMD ["bash"]
                        
# 2022-09-02 07:46:35  77.83MB 
/bin/sh -c #(nop) ADD file:a7268f82a86219801950401c224cabbdd83ef510a7c71396b25f70c2639ae4fa in / 
                        
                    

镜像信息

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    "RepoTags": [
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    "RepoDigests": [
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    ],
    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2025-02-14T21:51:04.124610926Z",
    "Container": "",
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